TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?

Abstract: In this paper, we introduce a novel visual representation learning which relies on a handful of adaptively learned tokens, and which is applicable to both image and video understanding tasks. Instead of relying on hand-designed splitting strategies to obtain visual tokens and processing a large number of densely sampled patches for attention, our approach learns to mine important tokens in visual data. This results in efficiently and effectively finding a few important visual tokens and enables modeling of pairwise attention between such tokens, over a longer temporal horizon for videos, or the spatial content in images. Our experiments demonstrate strong performance on several challenging benchmarks for both image and video recognition tasks. Importantly, due to our tokens being adaptive, we accomplish competitive results at significantly reduced compute amount. We obtain comparable results to the state-of-the-arts on ImageNet while being computationally more efficient. We also confirm the effectiveness of the approach on multiple video datasets, including Kinetics-400, Kinetics-600, Charades, and AViD. The code is available at: https://github.com/google-research/scenic/tree/main/scenic/projects/token_learner

• The paper introduces TokenLearner, a novel method that learns 8 tokens to dynamically capture salient image and video features with reduced computation.
• It employs a transformer-based approach that adaptively selects critical regions in visual data, enhancing efficiency and performance.
• Experiments demonstrate competitive accuracy on benchmark datasets, underscoring the method's potential for scalable, efficient visual recognition.

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